Computational medicine, present and the future: obstetrics and gynecology perspective

Abstract

Texas House of Representatives, Representative the 48th District (Rep.

Reducing Primary Cesarean Delivery Rate Through Implementation of a Smart Intrapartum Surveillance System in a Labor and Delivery Unit

“…The complexity and unpredictability of medicine in uence the work of obstetricians and nursing staff in labor and delivery units, where healthy patients with high expectations for their future are being cared for and where the incidences of malpractice lawsuits, defensive medical care, and unnecessary CS deliveries are high. Computational medicine has developed, with the objective for advancing health care by developing computational models for diseases, personalizing these models using patient data, and applying these models to improve the diagnosis and treatment of diseases; for obstetricians, these models assist in decision-making pertaining to maternity care 23 .…”

Utilizing machine learning to predict unplanned cesarean delivery

“…By developing models focusing on specific subgroups and specific gestations, previous models reduced the complexity of such interactions, while limiting generalizability and categorizing continuous features such as weight, BMI, and maternal age. The ML approach in general, 12,22 and tree ensemble methods, such as XGBoost specifically, 23 are capable of providing robust predictions by untangling complex, non-linear interactions between predictive features. Hence, in the presented model, features such as parity, labor induction, BMI, and maternal age rather than serving as inclusion or exclusion criteria serve as predictive features interacting with other features contributing to a comprehensive prediction of uCD.…”

Computational medicine: Grand challenges and opportunities for revolutionizing personalized healthcare

“…Computational medicine emerged in the past decade as an interdisciplinary field dedicated to integrating advanced computational modeling, data-driven technologies, and supercomputing to derive new knowledge about the biological mechanisms of disease and deeper understanding of factors driving inter-patient variability (Bukowski et al, 2021). Such knowledge enables development of precision strategies to diagnose and treat disease, sustain wellbeing, and optimize utilization of healthcare resources (Winslow et al, 2012).…”

“…Computational medicine has the potential to drive transformative advances in healthcare, extend health span, and reign in healthcare costs by i) enabling a more holistic understanding of the broad spectrum of all factors, processes, and their interplay impacting wellbeing at the individual and the population level, and by ii) translating such understanding into dynamically adaptive, personalized medical decisions to drive effective and sustainable health management practices. There are already several efforts demonstrating the potential of computational medicine across various diseases and conditions (e.g., (Louis et al, 2014;Mulder et al, 2018;Athanasiou et al, 2019;Bukowski et al, 2021;Yu and Kibbe, 2021;Hiram Guzzi et al, 2022;Toma et al, 2022).…”